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Motion artifacts in magnetic resonance imaging (MRI) arise from long acquisition times and can compromise the clinical utility of the images obtained. Traditional motion correction methods often struggle with severe motion, leading to distorted and unreliable results. Deep learning (DL) has improved upon these limitations through generalization, even though it also comes with challenges, such as vanishing structures and hallucinations.

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Abstract: IM-MoCo

  • 会议论文

  • 发表日期：

    2025年

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  • Ziad Al-Haj Hemidi,
  • Christian Weihsbach,
  • Mattias P. Heinrich

摘要

Motion artifacts in magnetic resonance imaging (MRI) arise from long acquisition times and can compromise the clinical utility of the images obtained. Traditional motion correction methods often struggle with severe motion, leading to distorted and unreliable results. Deep learning (DL) has improved upon these limitations through generalization, even though it also comes with challenges, such as vanishing structures and hallucinations.